Elite Cues and Mass Non-Compliance

80th Annual Midwest Political Science Association Conference

Zachary P Dickson & Sara B Hobolt

London School of Economics

Motivation & Research Questions

  • Public health & safety require compliance
    • Non-compliance can be costly
  • January 6th, 2021


  • Can elites motivate mass non-compliance with their rhetoric?
    • Can former-President Trump incite violent criminal behavior with his words?

Research Design

  • We leverage the fact that Trump called for the “liberation” of three specific states (MN, MI & VA) on April 17, 2020
  • Data – mobility (Meta 2022) & arrests (FBI 2022)
  • Time horizon – state lockdowns

How were the messages received?

Picture Note: Topic models include all quote tweets (143,171) of Trump’s LIBERATE tweets. A detailed description of text pre-processing and modeling methods are available in Appendix A.

Did the public respond?


Picture Note: Google Trends data are normalized and scaled according to time period and geography in order to represent the relative popularity of a search term on a range between 0 and 100 (Google 2020).

Mobility Estimation

  • Generalized Difference-in-Differences
    • Estimand = targeted cue
  • Treatment & control groups
    • Targeted [non-targeted] counties
  • Multiple models & counterfactuals
    • Targeted Republican [Democratic] counties vs non-targeted Republican [Democratic] counties

Results – Mobility


Table 1: Cummulative estimates: Mobility

(a) DV: Mobility
Full State Democratic Counties Republican Counties
Treatment 2.284* 1.005 2.706**
(0.906) (0.631) (0.854)
R2 0.764 0.825 0.714
(b) DV: Stay-at-home Compliance
Full State Democratic Counties Republican Counties
-1.128* -0.660 -1.336**
(0.502) (0.438) (0.476)
0.883 0.904 0.869

Note : + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Estimates are from two-way fixed effects models with county and time fixed effects. Standard errors are clustered by state and time. See Appendix D in paper for full results.

Dynamic Results – Mobility


Event Study Estimates for the effect of Trump’s calls for Liberation on Mobility in Red counties.

Note: April 17th is Day 1. Full results are presented in Appendix D

Crime

  • Identify crimes related to sentiment expressed in analysis of quote-tweets of Trump’s tweets

    • Daily statewide arrests for Disorderly conduct; Assault (Aggravated and Simple); Destruction/Damage/Vandalism of Property

  • 20-day window around Trump’s tweets (+/-10 days)

  • We use race as a crude proxy for partisanship

    • 6% of black voters and 28% of Hispanic voters supported Trump in 2016
    • 54% of whites, including 62% of white men

  • Counterfactual: statewide arrests of whites for the same crimes in non-targeted states

Results – Crime


Substantive effects:

  • \(e^{0.121} - 1 \approx 12.7\%\) increase in arrest rate of whites in states where Trump called for liberation
  • 483 total arrests in the treatment group
  • \(483/e^{0.121} \approx 55\) additional arrests of whites in states where Trump called for liberation
ATT: Arrest rate/100k
Estimate S.E. CI.lower CI.upper p.value
ATT.avg 0.121 0.054 0.015 0.226 0.025

Discussion & Concluding Remarks

  • Elites can motivate both compliance and non-compliant and even violent criminal behavior
    • Trump’s calls for liberation led to an increase in non-compliant behavior
    • These effects are concentrated in red counties (mobility) and among whites (crime)
  • Limitations
    • Estimates are conservative given counterfactuals
    • Is the American case unique?

Thank you!

References

Bisbee, James, and Diana Da In Lee. 2022. “Objective Facts and Elite Cues: Partisan Responses to Covid-19.” The Journal of Politics 84 (3): 1278–91.
FBI. 2022. “National Incident-Based Reporting System (NIBRS), Federal Bureau of Investigation.” https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs.
Google. 2020. “Google Trends: Search Term: “Liberate",” April. https://trends.google.com/trends/explore.
Grossman, Guy, Soojong Kim, Jonah M Rexer, and Harsha Thirumurthy. 2020. “Political Partisanship Influences Behavioral Responses to Governors’ Recommendations for COVID-19 Prevention in the United States.” Proceedings of the National Academy of Sciences 117 (39): 24144–53.
Meta. 2022. “Movement Range Maps.” https://data.humdata.org/dataset/movement-range-maps.

Robustness

  • Mobility
    • Alternative measure of mobility – Google mobility data (Appendix E)
      • Retail & recreation, and Aggregate mobility
    • Alternative estimation strategy – first-difference (Appendix F)
  • Crime
    • No effect of cues on arrest rate of other races (Appendix I)
    • No effect of cues on arrest rate of entire state population (Appendix J)
    • Alternative measurement of arrests – Two-day moving average (Appendix K)
    • Alternative modeling strategy – TWFE with state & date fixed effects (Appendix K)

Background – what do we already know?

  • Elite cues have differential effects on adherance to social distancing in Democratic and Republican counties
    • Grossman et al. (2020) show that US state governors’ were more effective at motivating social distancing behavior in Democratic-leaning counties than Republican-leaning counties
    • Bisbee and Lee (2022) show that reductive messages from President Trump play a similar role as objective information (COVID-19 cases/deaths) in influencing social distancing behavior
  • Non-compliance behavior?
  • Counterfactuals?

Elite Cues and Mass Non-Compliance | Dickson & Hobolt